plugins VS jaeger

Setting up Auth0 authentication in your Amplication application is easy. You can use the Auth0 plugin to add the required dependencies and configuration files to your application. The steps are as follows:

amplication/plugins

💡 Did you know? Amplication provides a Dockerfile for containerizing your services out of the box and has a plugin to create a Helm Chart for your services to ease container orchestration.

💡Pro Tip: Amplication now offers a Redis Plugin that can help you integrate Redis into your microservices more easily than ever before.

2. Helm Charts Plugin: Amplication offers a Helm Charts plugin that simplifies the deployment of containerized microservices on Kubernetes clusters. Helm charts provide a templated approach to defining Kubernetes resources, making it easier to manage the deployment process and ensure consistency across different environments.

To illustrate this development workflow, let's look at the MySQL plugin. But first, let's review the functionality of a database connection:

Hello, Yuval here, founder of Amplication. I’m very excited to share with you what the Amplication team is working on.

Amplication saves engineers from undifferentiated heavy lifting in application development, while helping organizations speed up delivery, and enforcing best practices and standardization across multiple teams and developers.

Amplication accelerates the development of backend applications and microservices with high-quality code generation, streamlining and automating development while solving issues that delay production readiness.

We built Amplication so developers like us could focus on coding the parts that matter rather than get distracted by repetitive tasks and boilerplate code.

Amplication is an open-source platform that lets you easily configure your backend services, by generating a human-readable and editable TypeScript Node.js codebase. The generated code includes everything you need to start writing your business logic.

Amplication continuously generates the code based on changes in the schema and configuration, then pushes the code to a GitHub repository to allow developers to continue off and edit it further based on their needs.

Today's launch of our v1.0 also incorporates a plugin architecture that enables developers to develop their own plugins to implement best practices, code conventions, custom integrations, and virtually anything in the generated code. Developers can use plugins created by Amplication’s core team, by our community, or create their own.

We already have several plugins on our Github plugins repo -https://github.com/amplication/plugins, including support for Kafka, MySQL PostgreSQL, Passport JWT, Passport Basic authentication, and the list is growing.

We can’t wait for you to experience Amplication, please share your thoughts.

Jaeger maps the flow of requests and data as they traverse a distributed system. These requests may make calls to multiple services, which may introduce their own delays or errors. https://www.jaegertracing.io/

As engineers at past startups, we often had to debug slow queries, poor load times, inconsistent errors, etc... While tools like Jaegar [2] helped us inspect server-side performance, we had no way to tie user events to the traces we were inspecting. In other words, although we had an idea of what API route was slow, there wasn’t much visibility into the actual bottleneck.

This is where our performance product comes in: we’re rethinking a tracing/performance tool that focuses on bridging the gap between the client and server.

What’s unique about our approach is that we lean heavily into creating traces from the frontend. For example, if you’re using our Next.js SDK, we automatically connect browser HTTP requests with server-side code execution, all from the perspective of a user. We find this much more powerful because you can understand what part of your frontend codebase causes a given trace to occur. There’s an example here [3].

From an instrumentation perspective, we’ve built our SDKs on-top of OTel, so you can create custom spans to expand highlight-created traces in server routes that will transparently roll up into the flame graph you see in our UI. You can also send us raw OTel traces and manually set up the client-server connection if you want. [4] Here’s an example of what a trace looks like with a database integration using our Golang GORM SDK, triggered by a frontend GraphQL query [5] [6].

In terms of how it's built, we continue to rely heavily on ClickHouse as our time-series storage engine. Given that traces require that we also query based on an ID for specific groups of spans (more akin to an OLTP db), we’ve leveraged the power of CH materialized views to make these operations efficient (described here [7]).

To try it out, you can spin up the project with our self hosted docs [8] or use our cloud offering at app.highlight.io. The entire stack runs in docker via a compose file, including an OpenTelemetry collector for data ingestion. You’ll need to point your SDK to export data to it by setting the relevant OTLP endpoint configuration (ie. environment variable OTEL_EXPORTER_OTLP_LOGS_ENDPOINT [9]).

Overall, we’d really appreciate feedback on what we’re building here. We’re also all ears if anyone has opinions on what they’d like to see in a product like this!

[1] https://github.com/highlight/highlight/blob/main/LICENSE

[2] https://www.jaegertracing.io

[3] https://app.highlight.io/1383/sessions/COu90Th4Qc3PVYTXbx9Xe...

[4] https://www.highlight.io/docs/getting-started/native-opentel...

[5] https://static.highlight.io/assets/docs/gorm.png

[6] https://github.com/highlight/highlight/blob/1fc9487a676409f1...

[7] https://highlight.io/blog/clickhouse-materialized-views

[8] https://www.highlight.io/docs/getting-started/self-host/self...

[9] https://opentelemetry.io/docs/concepts/sdk-configuration/otl...

For the request following, something like jeager https://www.jaegertracing.io/, because you are talking more about tracing than necessarily logging. For just monitoring, https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack would be the starting point, then it depends. Nginx gives metrics out of the box, then you can pull in the dashboard like https://grafana.com/grafana/dashboards/14314-kubernetes-nginx-ingress-controller-nextgen-devops-nirvana/ , or full metal with something like service mesh monitoring which would provably fulfil most of the requirements

Have you checked out Jaeger [1]? It is lightweight enough for a personal project, but featureful enough to really help "turn on the lightbulb" with other engineers to show them the difference between logging/monitoring and tracing.

[1] https://www.jaegertracing.io/

From the perspective of the realization of GraphQL infrastructure, the interesting direction is "Finding". How to find the problem? How to find the bottleneck of the system? Distributed Tracing System (DTS) will help answer this question. Distributed tracing is a method of observing requests as they propagate through distributed environments. In our scenario, we have dozens of subgraphs, gateway, and transport layer through which the request goes. We have several tools that can be used to detect the whole lifecycle of the request through the system, e.g. Jaeger, Zipkin or solutions that provided DTS as a part of the solution NewRelic.

Jaeger is an open-source, distributed tracing system that monitors and troubleshoots the flow of requests through complex, microservices-based applications, providing a comprehensive view of system interactions.

However, ensuring fault tolerance in distributed systems is not at all easy. These systems are complex, with multiple nodes or components working together. A failure in one node can cascade across the system if not addressed timely. Moreover, the inherently distributed nature of these systems can make it challenging to pinpoint the exact location and cause of fault - that is why modern systems rely heavily on distributed tracing solutions pioneered by Google Dapper and widely available now in Jaeger and OpenTracing. But still, understanding and implementing fault tolerance becomes not just about addressing the failure but predicting and mitigating potential risks before they escalate.

In this article, we'll use HoneyComb.io as our tracing backend. While there are other tools in the market, some of which can be run on your local machine (e.g., Jaeger), I chose HoneyComb because of their complementary tools that offer improved monitoring of the service and insights into its behavior.

The best way to do this, is with the help of tracing tools such as paid tools such as Honeycomb, or your own instance of the open source Jaeger offering, or perhaps Encore's built in tracing system.

In this example, I will create 3 Node.js services (shipping, notification, and courier) using Amplication, add traces to all services, and show how to analyze trace data using Jaeger.